“Get Social” with Enterprise Data to Speed and Improve Analytics Outcomes

Social media has worked its way into almost every aspect of our daily lives – both personally and professionally. Its on-demand nature has dramatically increased end-user expectations of the availability and timeliness of enterprise data. And the data collection methods inherent in social media platforms, such as crowdsourcing, are the envy of business users and analysts who desperately seek to share information and curated data sets across their organization (let’s face it, no one wants to re-invent the wheel). It’s time for organizations to transform the way they think about business data, and this means “getting social” with enterprise data to maximize analytics and business outcomes.

An Evolution in Data Accessibility and Self-Service

Self-service data preparation has significantly advanced in the last 12–18 months. Modern tools deliver access to dark data locked in semi-structured and unstructured data repositories, automated and pre-defined data preparation functions, direct exports of analytics-ready data to visualization tools, and business intelligence (BI) platforms, built-in automation and governance functionality for security and compliance, and more. However, despite all of these innovations, for most business users and analysts, data access is still limited to personal data sources, historical reports or data controlled by IT and BI gatekeepers – which can be outdated by the time they receive it. Far too many people are building Excel spreadsheets and reports in seclusion, using sources they can’t completely trust and/or making business decisions based on incomplete information.

But, data preparation and analytics don’t have to be this difficult, and there’s been an evolutionary leap to expedite, simplify and improve these processes by using data socialization.

Data socialization takes the fundamentals of social media – creating and sharing information – and brings it to the business world. In technical terms, it involves a central data-management platform that unites self-service visual data preparation, discovery, cataloging, stewardship, automation and governance with key attributes common to social-media platforms. It leverages popular social media and crowdsourcing features to make data readily accessible and easily sharable across an organization. It empowers business users and analysts to:

– Understand the relevancy of data in relation to how it’s used by different user roles, and follow key users and data sources (à la Twitter)

– Find, “like” and share data, and receive automatic notifications when new relevant content becomes available (à la Facebook)

– Build a network of influencers and collaborate to better harness the tribal knowledge that too often falls to the wayside (à la LinkedIn)

– Create a marketplace of enterprise and public data sets. As users work with requested data, machine learning technology identifies patterns of use and success, performs data quality scoring, suggests relevant sources and automatically recommends likely data preparation actions based on user persona (à la Amazon).